Add to ORKGThis work relates to the problem of linear approximation of multidimensional statistical data. Instead of the approach of regression analysis, we want to use another approach which is to minimize of the sum of the squares of the per-pendicular distances from the system of points to the approximating plane. We receive the formula of minimum distance from point to linear variety in Euclidean space of the two-dimensional matrices as a first step in solving the problem
The computation of the minimum distance of a point to a surface in a finite-dimensional space is a k...
Consider the problem of determining whether or not a partial dissimilarity matrix can be completed t...
AbstractLet S be a family of m convex polygons in the plane with a total number of n vertices and le...
This work relates to the problem of linear approximation of multidimensional statistical data. Inste...
k-Principal points of a random variable are k points that minimize the mean squared distance (MSD) b...
Consider the following problem: find the "best" approximating hyperplane for a family of points. The...
AbstractIn this paper, we study the problem of L1-fitting a shape to a set of n points in Rd (where ...
In most problems in mathematics, science, engineering, and economics it is sufficient to find an equ...
A Classical problem in matrix analysis and total least squares estimation is that of finding a best ...
AbstractA classical problem in matrix analysis and total least squares estimation is that of finding...
Given a finite set A in a normed linear space X and the integer k smaller or equal to the number of ...
The computation of the minimum distance of a point to a surface in a finite-dimensional space is a k...
AbstractIn this paper we determine the Euclidean distance matrix which is closest to a given (not ne...
AbstractThis paper proposes an algorithm for matrix minimum-distance projection, with respect to a m...
We seek for lines of minimal distance to finitely many given points in the plane. The distance betwe...
The computation of the minimum distance of a point to a surface in a finite-dimensional space is a k...
Consider the problem of determining whether or not a partial dissimilarity matrix can be completed t...
AbstractLet S be a family of m convex polygons in the plane with a total number of n vertices and le...
This work relates to the problem of linear approximation of multidimensional statistical data. Inste...
k-Principal points of a random variable are k points that minimize the mean squared distance (MSD) b...
Consider the following problem: find the "best" approximating hyperplane for a family of points. The...
AbstractIn this paper, we study the problem of L1-fitting a shape to a set of n points in Rd (where ...
In most problems in mathematics, science, engineering, and economics it is sufficient to find an equ...
A Classical problem in matrix analysis and total least squares estimation is that of finding a best ...
AbstractA classical problem in matrix analysis and total least squares estimation is that of finding...
Given a finite set A in a normed linear space X and the integer k smaller or equal to the number of ...
The computation of the minimum distance of a point to a surface in a finite-dimensional space is a k...
AbstractIn this paper we determine the Euclidean distance matrix which is closest to a given (not ne...
AbstractThis paper proposes an algorithm for matrix minimum-distance projection, with respect to a m...
We seek for lines of minimal distance to finitely many given points in the plane. The distance betwe...
The computation of the minimum distance of a point to a surface in a finite-dimensional space is a k...
Consider the problem of determining whether or not a partial dissimilarity matrix can be completed t...
AbstractLet S be a family of m convex polygons in the plane with a total number of n vertices and le...